MySQL in Machine Learning Projects: The Data Foundation

Machine learning models are only as good as their data pipeline, and that pipeline usually starts and ends at a relational database. Here's where MySQL fits in the ML lifecycle.

Key Points

• Training data: SQL joins assemble features and labels from source tables
• Feature engineering: per-entity aggregates (counts, sums, recency) computed in SQL
• Point-in-time correctness: filter features to data available before the label date
• Reproducibility: save the exact extraction query with the model version
• Serving: write predictions back to MySQL for applications to read
• Scale note: for huge datasets, MySQL feeds warehouses like BigQuery downstream